Coffee Dadatset EDA Project

#### Coffee_EDA_Questions

1) Read the dataset<br>
2) Remove/handle the null values<br>
3) Depict different Market types and their count on a bar chart<br>
4) Depict different Product types and their count on a line chart<br>
5) Depict different State and their count on a pie chart<br>
6) Depict Market wise sum of Profit and depict the result on bar chart.<br>
7) Depict Product wise mean of Sales and depict the result on bar chart and line chart superimposed<br>
8) Depcit Profit vs Sales on a Scatter Chart where Product is 'Regular Espresso'<br>
9) Depcit Budget Profit vs Budget Sales on a Scatter Chart where State is California and Product is 'Earl Grey'<br>
10) Depcit Market wise mean of Budget Sales and mean of Sales in a stacked bar chart<br>
11) Plot the Frequency distribution of Total Expenses<br>
12) Compare Frequency Distribution of Profit and Budget Profit on a histogram for the Oregon State<br>
13) Compare Frequency of COGS and Budget COGS on a histogram for the Eastern Market for the Product Cafe Mocha<br>
14) Compare Sales vs Profit on a histogram for the Eastern or Central Market and for Decaf Type<br>
15) Depict the quartile distribution of COGS where Sales is greater than 400<br>
16) Depict the quartile distribution of Budget Profit for Decaf Espresso<br>
17) Find the Market, Product and Type where Sales is maximum<br>
18) Find State and Market with Maximum Profit or Maximum Budget Profit<br>
19) Find Product, State and Market where Total Exepnses are minimum<br>
20) Statisically infer the State, Product and Market chosen to further expnad/invest into the Coffee Business based on Sales, Profit and Total Expenses<br>
21) Statisically infer the State, Product and Market chosen to further withhold the Coffee Business based on Sales, Profit and Total Expenses.


22) Depict Market and Type wise sum of Budget Profit on a bar chart<br>
23) Depict Product Type and State wise mean of Sales on a area chart<br>
24) Depict Product and Type wise sum of Budget COGS on a line Chart<br>
25) Depict Product and Type wise sum of Budget Profit and Budget Sales on a line Chart<br>
26) Depict Market and Product Type based mean of Sales on abar chart and on a stacked bar chart separately<br>
27) Find State and Market Size based mean of Sales and COGS where Market is not Central and Profit is over 350. Depict the result on a line chart, stacked bar and area chart separately<br>
28) Find Product and Market Size based mean of Budget COGS and Inventory where Type is Regular and neither product or its Type is Espresso. Depict the result on a line chart, stacked bar and area chart separately <br>


<b>Seaborn EDA</b><br>

1) Depict Product based on Market Size on a count plot<br>
2) Depict State based on Market on a count plot<br>
3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>
4) Depict Market vs Budget COGS on a boxplot<br>
5) Depict Product vs Margin on a boxplot based on Market Size<br>
6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling<br>
7) Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<br>
8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
9)  Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
10) Generate Pairplot for the Coffee dataset where Market is Western or Central based and Product Line is beans based on Type including fields- COGS, Sales, Proift and Total Expenses<br>
11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17)  Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>

df.isnull().sum()

Area Code         0
Date              0
Market            0
Market Size       0
Product           0
Product Line      0
Product Type      0
State             0
Type              0
Budget COGS       0
Budget Margin     0
Budget Profit     0
Budget Sales      0
COGS              0
Inventory         0
Margin            0
Marketing         0
Profit            0
Sales             0
Total Expenses    0
dtype: int64

d2 = df['Product Type'].value_counts()
d2

Espresso      1176
Coffee        1056
Herbal Tea    1056
Tea            960
Name: Product Type, dtype: int64

from collections import Counter

d4 = df['State'].value_counts()
d4
#m = Counter(df['State'])
#print(m)
#plt.pie(m,labels='State')
#plt.show

California       288
Utah             288
Nevada           264
Oregon           264
Colorado         264
Washington       240
Illinois         216
Ohio             216
Missouri         216
Wisconsin        216
Florida          216
Iowa             216
New York         192
New Hampshire    168
Texas            168
Louisiana        168
Connecticut      168
Oklahoma         168
New Mexico       168
Massachusetts    144
Name: State, dtype: int64

plt.figure(figsize=(6,6))
plt.plot(d4.index,d4,marker='o')
plt.xticks(d4.index,d4,rotation=90)
plt.pie(d4,labels=d4.index,autopct='%0.2f%%')
#plt.pie(d4.index,labels='State')
plt.show()

import matplotlib.pyplot as plt

plt.bar(d2.index,d2,color='red')
plt.plot(d2.index,d2,color='green',marker='o')
plt.xlabel('product type')
plt.ylabel('product count')
plt.show()

# in case of multiindex series or dataframe we will use pandas chart
#
when two column or more came it is data frame 

#df.groupby(['Market'],['Type'])['Budget Profit'].sum()

plt.bar

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv("Coffee.csv")
df
o = df.groupby(['Market','Type'])['Budget Profit'].sum()
print(o)
#plt.pie(o,labels=j,shadow=False,autopct='%0.2f%%')
#plt.show()

Market   Type   
Central  Decaf      43880
         Regular    48700
East     Decaf      10800
         Regular    45980
South    Decaf      16320
         Regular    18720
West     Decaf      36240
         Regular    38120
Name: Budget Profit, dtype: int64

from collections import Counter

d4 = df['State'].value_counts()
d4

Utah             288
California       288
Oregon           264
Colorado         264
Nevada           264
Washington       240
Missouri         216
Illinois         216
Wisconsin        216
Iowa             216
Florida          216
Ohio             216
New York         192
Connecticut      168
Oklahoma         168
Texas            168
New Mexico       168
New Hampshire    168
Louisiana        168
Massachusetts    144
Name: State, dtype: int64

plt.figure(figsize=(6,6))
plt.plot(d4.index,d4,marker='o')
plt.xticks(d4.index,d4,rotation=90)
plt.pie(d4,labels=d4.index,autopct='%0.2f%%')
#plt.pie(d4.index,labels='State')
plt.show()

df

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
0	719	40909	Central	Major Market	Amaretto	Beans	Coffee	Colorado	Regular	90	130	100	220	89	777	130	24	94	219	36
1	970	40909	Central	Major Market	Colombian	Beans	Coffee	Colorado	Regular	80	110	80	190	83	623	107	27	68	190	39
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
3	303	40909	Central	Major Market	Green Tea	Leaves	Tea	Colorado	Regular	30	50	30	80	44	623	56	14	30	100	26
4	303	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Colorado	Regular	60	90	70	150	54	456	80	15	54	134	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4243	206	41609	West	Small Market	Caffe Latte	Beans	Espresso	Washington	Regular	20	30	20	50	24	567	32	7	19	60	19
4244	509	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Washington	Regular	60	80	30	140	65	403	80	24	34	155	57
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45
4246	360	41609	West	Small Market	Colombian	Beans	Coffee	Washington	Regular	80	120	80	200	72	461	104	23	86	188	46
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

4248 rows × 20 columns

df.shape

(4248, 20)

j = df['Market'].unique().tolist()
print(j)
#print([j.index])

['Central', 'East', 'South', 'West']

u = df['Type'].unique().tolist()
print(u)

['Regular', 'Decaf']

df.info

<bound method DataFrame.info of       Area Code   Date   Market   Market Size            Product Product Line  \
0           719  40909  Central  Major Market           Amaretto        Beans   
1           970  40909  Central  Major Market          Colombian        Beans   
2           970  40909  Central  Major Market  Decaf Irish Cream        Beans   
3           303  40909  Central  Major Market          Green Tea       Leaves   
4           303  40909  Central  Major Market        Caffe Mocha        Beans   
...         ...    ...      ...           ...                ...          ...   
4243        206  41609     West  Small Market        Caffe Latte        Beans   
4244        509  41609     West  Small Market        Caffe Mocha        Beans   
4245        360  41609     West  Small Market     Decaf Espresso        Beans   
4246        360  41609     West  Small Market          Colombian        Beans   
4247        206  41609     West  Small Market  Decaf Irish Cream        Beans   

     Product Type       State     Type  Budget COGS  Budget Margin  \
0          Coffee    Colorado  Regular           90            130   
1          Coffee    Colorado  Regular           80            110   
2          Coffee    Colorado    Decaf          100            140   
3             Tea    Colorado  Regular           30             50   
4        Espresso    Colorado  Regular           60             90   
...           ...         ...      ...          ...            ...   
4243     Espresso  Washington  Regular           20             30   
4244     Espresso  Washington  Regular           60             80   
4245     Espresso  Washington    Decaf           70            100   
4246       Coffee  Washington  Regular           80            120   
4247       Coffee  Washington    Decaf          120            170   

      Budget Profit  Budget Sales  COGS  Inventory  Margin  Marketing  Profit  \
0               100           220    89        777     130         24      94   
1                80           190    83        623     107         27      68   
2               110           240    95        821     139         26     101   
3                30            80    44        623      56         14      30   
4                70           150    54        456      80         15      54   
...             ...           ...   ...        ...     ...        ...     ...   
4243             20            50    24        567      32          7      19   
4244             30           140    65        403      80         24      34   
4245             60           170    80       1079      96         24      76   
4246             80           200    72        461     104         23      86   
4247             50           290   105        716     145         95      30   

      Sales  Total Expenses  
0       219              36  
1       190              39  
2       234              38  
3       100              26  
4       134              26  
...     ...             ...  
4243     60              19  
4244    155              57  
4245    188              45  
4246    188              46  
4247    266             125  

[4248 rows x 20 columns]>

df.describe

<bound method NDFrame.describe of       Area Code   Date   Market   Market Size            Product Product Line  \
0           719  40909  Central  Major Market           Amaretto        Beans   
1           970  40909  Central  Major Market          Colombian        Beans   
2           970  40909  Central  Major Market  Decaf Irish Cream        Beans   
3           303  40909  Central  Major Market          Green Tea       Leaves   
4           303  40909  Central  Major Market        Caffe Mocha        Beans   
...         ...    ...      ...           ...                ...          ...   
4243        206  41609     West  Small Market        Caffe Latte        Beans   
4244        509  41609     West  Small Market        Caffe Mocha        Beans   
4245        360  41609     West  Small Market     Decaf Espresso        Beans   
4246        360  41609     West  Small Market          Colombian        Beans   
4247        206  41609     West  Small Market  Decaf Irish Cream        Beans   

     Product Type       State     Type  Budget COGS  Budget Margin  \
0          Coffee    Colorado  Regular           90            130   
1          Coffee    Colorado  Regular           80            110   
2          Coffee    Colorado    Decaf          100            140   
3             Tea    Colorado  Regular           30             50   
4        Espresso    Colorado  Regular           60             90   
...           ...         ...      ...          ...            ...   
4243     Espresso  Washington  Regular           20             30   
4244     Espresso  Washington  Regular           60             80   
4245     Espresso  Washington    Decaf           70            100   
4246       Coffee  Washington  Regular           80            120   
4247       Coffee  Washington    Decaf          120            170   

      Budget Profit  Budget Sales  COGS  Inventory  Margin  Marketing  Profit  \
0               100           220    89        777     130         24      94   
1                80           190    83        623     107         27      68   
2               110           240    95        821     139         26     101   
3                30            80    44        623      56         14      30   
4                70           150    54        456      80         15      54   
...             ...           ...   ...        ...     ...        ...     ...   
4243             20            50    24        567      32          7      19   
4244             30           140    65        403      80         24      34   
4245             60           170    80       1079      96         24      76   
4246             80           200    72        461     104         23      86   
4247             50           290   105        716     145         95      30   

      Sales  Total Expenses  
0       219              36  
1       190              39  
2       234              38  
3       100              26  
4       134              26  
...     ...             ...  
4243     60              19  
4244    155              57  
4245    188              45  
4246    188              46  
4247    266             125  

[4248 rows x 20 columns]>

df.fillna(method='bfill')

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
0	719	40909	Central	Major Market	Amaretto	Beans	Coffee	Colorado	Regular	90	130	100	220	89	777	130	24	94	219	36
1	970	40909	Central	Major Market	Colombian	Beans	Coffee	Colorado	Regular	80	110	80	190	83	623	107	27	68	190	39
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
3	303	40909	Central	Major Market	Green Tea	Leaves	Tea	Colorado	Regular	30	50	30	80	44	623	56	14	30	100	26
4	303	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Colorado	Regular	60	90	70	150	54	456	80	15	54	134	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4243	206	41609	West	Small Market	Caffe Latte	Beans	Espresso	Washington	Regular	20	30	20	50	24	567	32	7	19	60	19
4244	509	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Washington	Regular	60	80	30	140	65	403	80	24	34	155	57
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45
4246	360	41609	West	Small Market	Colombian	Beans	Coffee	Washington	Regular	80	120	80	200	72	461	104	23	86	188	46
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

4248 rows × 20 columns

print(df['Market Size'].nunique())

2

print(df['Product'].count())

4248

m= df['Product'].tolist()
print(m)

['Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green 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Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Lemon', 'Mint', 'Darjeeling', 'Colombian', 'Caffe Mocha', 'Decaf Espresso', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Green Tea', 'Lemon', 'Darjeeling', 'Colombian', 'Caffe Mocha', 'Regular Espresso', 'Green Tea', 'Lemon', 'Darjeeling', 'Amaretto', 'Colombian', 'Caffe Mocha', 'Regular Espresso', 'Green Tea', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Caffe Mocha', 'Regular Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream']

print(df['State'].count())

4248

d2 = df['Product Type'].value_counts()
d2

Espresso      1176
Coffee        1056
Herbal Tea    1056
Tea            960
Name: Product Type, dtype: int64

#plt.bar(d2.index,d2,color='red')
plt.plot(d2.index,d2,color='green',marker='o')
plt.xlabel('product type')
plt.ylabel('product count')
plt.show()

#d8 = df['State'].value_counts()
#d8
h = Counter

d5 = df[df['Product'] == 'Regular Espresso']
d5.shape

(72, 20)

import seaborn as sns

sns.scatterplot(x = d5['Profit'],y= d5['Sales'],color="maroon")
plt.show()

d7 = df.groupby('Market')['Profit'].sum()
d7

Market
Central    93852
East       59217
South      32478
West       73996
Name: Profit, dtype: int64

plt.bar(d7.index,d7,color = 'red',label='Profit')
plt.xlabel('profit')
plt.ylabel('market')
#plt.xticks(d7.index,d7,rotation=90)
plt.show()

d9 = df.groupby('Product')['Sales'].mean()
d9
plt.bar(d9.index,d9,color = 'lightblue')
plt.plot(d9.index,d9,color = 'maroon',marker = 'o')
plt.xlabel('Product')
plt.ylabel('Sales')
plt.legend()
plt.title('Product vs Sales mean')
plt.xticks(rotation =60)
plt.show()

No handles with labels found to put in legend.

dfg = df.groupby('Market')['Budget Sales','Sales'].mean()
dfg
#dfg.shape

<ipython-input-29-56b6c924d30f>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  dfg = df.groupby('Market')['Budget Sales','Sales'].mean()

	Budget Sales	Sales
Market
Central	186.279762	197.206101
East	177.815315	201.099099
South	146.130952	154.651786
West	178.348214	202.577381

#plt.plot(kind='Bar',stack=True,color='maroon')
#plt.bar(dfg.index,dfg,label='budget sales vs sales')
#plt.title('market wise budget sales and sales distribution')
#plt.show()

df11 = df[(df['State'] == 'California') & (df['Product'] == 'Earl Grey')]
df11.shape 
df11

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
140	650	40909	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	60	60	90	54	456	80	15	54	134	26
317	858	40940	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	70	60	120	77	449	112	21	80	189	32
495	951	40969	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	441	117	22	84	198	33
674	530	41000	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	460	117	22	83	198	34
851	530	41030	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	90	80	140	86	499	124	24	89	210	35
1027	415	41061	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	90	80	150	94	562	136	26	98	230	38
1201	831	41091	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	90	80	150	94	608	136	26	98	230	38
1378	310	41122	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	100	90	160	100	598	145	28	105	245	40
1555	714	41153	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	588	118	22	84	199	34
1730	209	41183	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	50	50	80	76	580	111	21	79	187	32
1907	650	41214	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	50	40	80	50	589	73	14	47	123	26
2084	619	41244	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	60	50	90	54	601	79	15	53	133	26
2261	707	41275	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	60	60	90	54	456	80	15	84	150	27
2438	707	41306	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	70	60	120	77	449	112	21	119	201	32
2615	916	41334	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	441	117	22	125	211	33
2792	707	41365	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	460	117	22	123	211	34
2969	562	41395	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	90	80	140	86	499	124	24	123	208	33
3146	925	41426	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	90	80	150	94	562	136	26	135	228	35
3319	707	41456	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	90	80	150	94	608	136	26	135	228	35
3497	760	41487	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	60	100	90	160	100	598	145	28	145	243	37
3674	650	41518	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	50	80	70	130	81	588	118	22	125	212	34
3851	626	41548	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	50	50	80	76	580	111	21	117	199	32
4028	760	41579	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	50	40	80	50	589	73	14	70	131	26
4205	408	41609	West	Major Market	Earl Grey	Leaves	Tea	California	Regular	30	60	50	90	54	601	79	15	79	142	26

sns.scatterplot(x = df11['Budget Sales'],y = df11['Budget Profit'],color="green")
plt.title('Budget Profit-Budget sales for product earl grey for california state')
plt.xlabel('Budget Profit')
plt.ylabel('Budget Sales')
plt.show()

plt.hist(x = df['Total Expenses'],bins='auto',label='frequency',color='tan')
plt.show()

dfff = df[df['Sales'] > 400]
dfff

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
13	309	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Illinois	Regular	270	370	260	640	234	1310	312	77	203	546	109
14	630	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Illinois	Decaf	260	270	180	530	228	1459	228	63	140	456	88
25	641	40909	Central	Small Market	Chamomile	Leaves	Herbal Tea	Iowa	Decaf	200	280	200	480	234	1310	312	77	202	546	110
27	712	40909	Central	Small Market	Darjeeling	Leaves	Tea	Iowa	Regular	190	210	140	400	228	1459	228	63	141	456	87
28	319	40909	Central	Small Market	Earl Grey	Leaves	Tea	Iowa	Regular	210	270	160	480	245	1419	301	93	175	546	126
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4212	209	41609	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	240	310	210	550	247	1744	329	81	321	614	113
4214	530	41609	West	Major Market	Colombian	Beans	Coffee	California	Regular	330	500	350	830	279	2642	420	97	402	745	149
4216	775	41609	West	Small Market	Darjeeling	Leaves	Tea	Nevada	Regular	180	240	150	420	247	1744	329	81	319	614	114
4217	775	41609	West	Small Market	Earl Grey	Leaves	Tea	Nevada	Regular	180	180	100	360	250	1820	251	70	233	534	94
4220	702	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Nevada	Decaf	210	280	170	490	224	1191	310	73	288	569	116

418 rows × 20 columns

dfff['COGS']

13      234
14      228
25      234
27      228
28      245
       ... 
4212    247
4214    279
4216    247
4217    250
4220    224
Name: COGS, Length: 418, dtype: int64

print(dfff['COGS'])
print(len(dfff['COGS']))
Q0 = np.percentile(dfff['COGS'], 0)    
Q1 = np.percentile(dfff['COGS'], 25) 
Q2 = np.percentile(dfff['COGS'], 50)
Q3 = np.percentile(dfff['COGS'], 75) 
Q4 = np.percentile(dfff['COGS'], 100) 
  
IQR = Q3 - Q1 
print('Q0',Q0)
print('Q1',Q1)  # 1st quartile
print('Q2',Q2) # meadin
print('Q3',Q3) # 3rd quartile
print('Q4',Q4)
print('IQR',IQR)

13      234
14      228
25      234
27      228
28      245
       ... 
4212    247
4214    279
4216    247
4217    250
4220    224
Name: COGS, Length: 418, dtype: int64
418
Q0 52.0
Q1 228.0
Q2 247.0
Q3 265.0
Q4 364.0
IQR 37.0

df

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
0	719	40909	Central	Major Market	Amaretto	Beans	Coffee	Colorado	Regular	90	130	100	220	89	777	130	24	94	219	36
1	970	40909	Central	Major Market	Colombian	Beans	Coffee	Colorado	Regular	80	110	80	190	83	623	107	27	68	190	39
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
3	303	40909	Central	Major Market	Green Tea	Leaves	Tea	Colorado	Regular	30	50	30	80	44	623	56	14	30	100	26
4	303	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Colorado	Regular	60	90	70	150	54	456	80	15	54	134	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4243	206	41609	West	Small Market	Caffe Latte	Beans	Espresso	Washington	Regular	20	30	20	50	24	567	32	7	19	60	19
4244	509	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Washington	Regular	60	80	30	140	65	403	80	24	34	155	57
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45
4246	360	41609	West	Small Market	Colombian	Beans	Coffee	Washington	Regular	80	120	80	200	72	461	104	23	86	188	46
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

4248 rows × 20 columns

dffr = df[(df['Product Type']== 'Espresso') & (df['Type'] == 'Decaf')]
dffr

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
5	720	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Colorado	Decaf	80	130	80	210	72	558	108	23	53	180	55
14	630	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Illinois	Decaf	260	270	180	530	228	1459	228	63	140	456	88
24	563	40909	Central	Small Market	Decaf Espresso	Beans	Espresso	Iowa	Decaf	10	40	30	50	17	777	26	4	10	43	16
33	660	40909	Central	Small Market	Decaf Espresso	Beans	Espresso	Missouri	Decaf	50	80	60	130	46	777	68	12	45	114	23
42	614	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Ohio	Decaf	60	90	40	150	58	338	72	22	16	130	56
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4200	435	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Utah	Decaf	40	70	50	110	50	898	73	14	70	131	26
4212	209	41609	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	240	310	210	550	247	1744	329	81	321	614	113
4224	775	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Nevada	Decaf	20	30	20	50	23	807	35	6	25	62	18
4234	971	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Oregon	Decaf	150	140	80	290	153	1319	153	42	129	326	66
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45

408 rows × 20 columns

print(dffr['Budget Profit'])
print(len(dffr['Budget Profit']))
Q0 = np.percentile(dffr['Budget Profit'], 0)    
Q1 = np.percentile(dffr['Budget Profit'], 25) 
Q2 = np.percentile(dffr['Budget Profit'], 50)
Q3 = np.percentile(dffr['Budget Profit'], 75) 
Q4 = np.percentile(dffr['Budget Profit'], 100) 
  
IQR = Q3 - Q1 
print('Q0',Q0)
print('Q1',Q1)  # 1st quartile
print('Q2',Q2) # meadin
print('Q3',Q3) # 3rd quartile
print('Q4',Q4)
print('IQR',IQR)

5        80
14      180
24       30
33       60
42       40
       ... 
4200     50
4212    210
4224     20
4234     80
4245     60
Name: Budget Profit, Length: 408, dtype: int64
408
Q0 0.0
Q1 40.0
Q2 50.0
Q3 82.5
Q4 300.0
IQR 42.5

sales_min = df['Sales'].min()
print(sales_min)
profit_min = df['Profit'].min()
print(profit_min)
total_expense = df['Total Expenses'].max()
print(total_expense)

17
-638
190

d34 = df[(df['Sales']==17)| (df['Profit']==-638)|(df['Total Expenses']==190)]
d34

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
154	702	40909	West	Small Market	Green Tea	Leaves	Tea	Nevada	Regular	160	-160	-240	0	245	1419	-245	93	-354	17	126
1385	775	41122	West	Small Market	Chamomile	Leaves	Herbal Tea	Nevada	Decaf	140	220	70	360	173	927	239	156	49	412	190
3687	775	41518	West	Small Market	Green Tea	Leaves	Tea	Nevada	Regular	190	-190	-280	0	302	6413	-302	114	-638	20	147

d34[['State','Market','Product','Type','Sales','Profit','Total Expenses']]

	State	Market	Product	Type	Sales	Profit	Total Expenses
154	Nevada	West	Green Tea	Regular	17	-354	126
1385	Nevada	West	Chamomile	Decaf	412	49	190
3687	Nevada	West	Green Tea	Regular	20	-638	147

sales_max = df['Sales'].max()
print(sales_max)
profit_max = df['Profit'].max()
print(profit_max)
total_expense = df['Total Expenses'].min()
print(total_expense)

912
778
10

d33 = df[(df['Sales']==912) | (df['Profit']==778) | (df['Total Expenses']==10)]
#d33.shape
d33

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
1154	845	41091	East	Major Market	Colombian	Beans	Coffee	New York	Regular	340	520	370	860	364	2654	548	127	370	912	178
1196	707	41091	West	Major Market	Colombian	Beans	Coffee	California	Regular	450	690	530	1140	364	2654	548	127	370	912	178
2910	603	41395	East	Small Market	Green Tea	Leaves	Tea	New Hampshire	Regular	0	40	30	40	0	645	43	0	44	43	10
3087	603	41426	East	Small Market	Green Tea	Leaves	Tea	New Hampshire	Regular	0	40	30	40	0	602	43	0	44	43	10
3263	978	41456	East	Major Market	Colombian	Beans	Coffee	Massachusetts	Regular	40	590	560	630	52	-1493	613	17	778	659	46
3339	775	41456	West	Small Market	Caffe Latte	Beans	Espresso	Nevada	Regular	0	40	30	40	0	559	43	0	44	43	10
3516	775	41487	West	Small Market	Caffe Latte	Beans	Espresso	Nevada	Regular	0	40	30	40	0	516	43	0	44	43	10

moto = pd.concat([df['Product'],df['State'],df['Market'],df['Total Expenses']],axis=1)
moto
#moto.shape

	Product	State	Market	Total Expenses
0	Amaretto	Colorado	Central	36
1	Colombian	Colorado	Central	39
2	Decaf Irish Cream	Colorado	Central	38
3	Green Tea	Colorado	Central	26
4	Caffe Mocha	Colorado	Central	26
...	...	...	...	...
4243	Caffe Latte	Washington	West	19
4244	Caffe Mocha	Washington	West	57
4245	Decaf Espresso	Washington	West	45
4246	Colombian	Washington	West	46
4247	Decaf Irish Cream	Washington	West	125

4248 rows × 4 columns

d33[['State','Market','Product','Type','Sales','Profit','Total Expenses']]

	State	Market	Product	Type	Sales	Profit	Total Expenses
1154	New York	East	Colombian	Regular	912	370	178
1196	California	West	Colombian	Regular	912	370	178
2910	New Hampshire	East	Green Tea	Regular	43	44	10
3087	New Hampshire	East	Green Tea	Regular	43	44	10
3263	Massachusetts	East	Colombian	Regular	659	778	46
3339	Nevada	West	Caffe Latte	Regular	43	44	10
3516	Nevada	West	Caffe Latte	Regular	43	44	10

dh = df.groupby(['Market','Type'])['Budget Profit'].sum()
dh

Market   Type   
Central  Decaf      43880
         Regular    48700
East     Decaf      10800
         Regular    45980
South    Decaf      16320
         Regular    18720
West     Decaf      36240
         Regular    38120
Name: Budget Profit, dtype: int64

dh.plot(kind='bar',color='maroon')
plt.title('Market and Type wise Budget Profit')
plt.xlabel('Budget Profit')
plt.ylabel('Market and Type')
plt.legend()
plt.xticks(rotation=45)
plt.show()

dc = df.groupby(['Product Type','State'])['Sales'].mean()
dc

Product Type  State      
Coffee        California     343.027778
              Colorado       229.819444
              Connecticut    288.458333
              Florida        249.458333
              Illinois       376.270833
                                ...    
Tea           Ohio           249.458333
              Oregon         185.583333
              Utah            92.638889
              Washington     135.888889
              Wisconsin      131.375000
Name: Sales, Length: 76, dtype: float64

dc.plot(kind='area',color='green')
plt.title('Product type vs state sales')
plt.xlabel('product type')
plt.ylabel('sales mean')
plt.xticks(rotation=60)
plt.show()

12#q
d14 = df[df['State']=='Oregon']
d14.shape

(264, 20)

plt.hist(d14['Profit'],bins='auto',color='red',label='Profit')
plt.hist(d14['Budget Profit'],bins='auto',label='green')
plt.legend()
plt.show()

#q13
d15 = df[(df['Market']=='East') & (df['Product']=='Caffe Mocha')]
d15

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
59	860	40909	East	Small Market	Caffe Mocha	Beans	Espresso	Connecticut	Regular	70	100	20	170	75	522	105	68	7	180	98
66	239	40909	East	Major Market	Caffe Mocha	Beans	Espresso	Florida	Regular	70	110	30	180	79	551	111	72	9	190	102
75	781	40909	East	Major Market	Caffe Mocha	Beans	Espresso	Massachusetts	Regular	60	60	-10	120	63	435	63	57	-23	126	86
81	603	40909	East	Small Market	Caffe Mocha	Beans	Espresso	New Hampshire	Regular	30	50	0	80	37	261	53	34	-10	90	63
89	315	40909	East	Major Market	Caffe Mocha	Beans	Espresso	New York	Regular	100	-50	-170	50	105	725	-44	95	-172	61	128
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4131	203	41609	East	Small Market	Caffe Mocha	Beans	Espresso	Connecticut	Regular	50	70	10	120	55	627	76	49	-3	140	78
4139	305	41609	East	Major Market	Caffe Mocha	Beans	Espresso	Florida	Regular	100	140	30	240	105	716	145	95	31	266	124
4146	978	41609	East	Major Market	Caffe Mocha	Beans	Espresso	Massachusetts	Regular	40	50	-10	90	47	571	52	42	-30	106	72
4152	603	41609	East	Small Market	Caffe Mocha	Beans	Espresso	New Hampshire	Regular	40	70	10	110	49	335	69	44	-6	126	73
4161	845	41609	East	Major Market	Caffe Mocha	Beans	Espresso	New York	Regular	130	-70	-210	60	135	3641	-69	122	-332	70	155

120 rows × 20 columns

#Q13
plt.hist(d15['COGS'],bins='auto',color='blue',label='COGS')
plt.hist(d15['Budget COGS'],bins='auto',color='green',label='Budget COGS')
plt.legend()
plt.show()

#Q14
d18 = df[((df['Market']=='East')|(df['Market']=='Central')) & (df['Type']=='Decaf')]
d18

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
5	720	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Colorado	Decaf	80	130	80	210	72	558	108	23	53	180	55
6	970	40909	Central	Major Market	Chamomile	Leaves	Herbal Tea	Colorado	Decaf	140	160	110	300	170	1091	171	47	99	341	72
7	719	40909	Central	Major Market	Lemon	Leaves	Herbal Tea	Colorado	Decaf	50	80	20	130	63	435	87	57	0	150	87
8	970	40909	Central	Major Market	Mint	Leaves	Herbal Tea	Colorado	Decaf	50	70	40	120	60	336	80	19	33	140	47
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4142	727	41609	East	Major Market	Decaf Irish Cream	Beans	Coffee	Florida	Decaf	120	160	110	280	134	690	186	41	181	341	64
4143	774	41609	East	Major Market	Lemon	Leaves	Herbal Tea	Massachusetts	Decaf	30	40	10	70	46	422	57	17	10	110	50
4149	603	41609	East	Small Market	Lemon	Leaves	Herbal Tea	New Hampshire	Decaf	20	40	10	60	39	250	49	14	3	94	47
4156	631	41609	East	Major Market	Lemon	Leaves	Herbal Tea	New York	Decaf	220	320	210	540	294	1727	453	111	459	796	144
4157	716	41609	East	Major Market	Mint	Leaves	Herbal Tea	New York	Decaf	180	-80	-150	100	241	5121	-93	74	-280	158	96

912 rows × 20 columns

plt.hist(d18['Sales'],bins='auto',color='maroon',label='Sales')
plt.hist(d18['Profit'],bins='auto',color='tan',label='Profit')
plt.legend()
plt.show()

d10 = df.groupby('Market')['Budget Sales'].mean()
d10

Market
Central    186.279762
East       177.815315
South      146.130952
West       178.348214
Name: Budget Sales, dtype: float64

d20 = df.groupby('Market')['Sales'].mean()
d20

Market
Central    197.206101
East       201.099099
South      154.651786
West       202.577381
Name: Sales, dtype: float64

#Q10
plt.bar(d20.index,d20,color='green')
plt.bar(d10.index,d10,color='maroon')
plt.title('market wise mean of budget sales and sales ')
plt.xlabel('Market')
plt.ylabel('sales and budget sales mean')
plt.show()

###Depict Product Type and State wise mean of Sales on a area chart<br>
#24) Depict Product and Type wise sum of Budget COGS on a line Chart<br>
#25) Depict Product and Type wise sum of Budget Profit and Budget Sales on a line Chart<br>
#26) Depict Market and Product Type based mean of Sales on abar chart and on a stacked bar chart separately<br>
#27) Find State and Market Size based mean of Sales and COGS where Market is not Central and Profit is over 350. Depict the result on a line chart, stacked bar and area chart separately<br>
#28) Find Product and Market Size based mean of Budget COGS and Inventory where Type is Regular and neither product or its Type is Espresso. Depict the result on a line chart, stacked bar and area ch

df22 = df[(df['Market']!='Central')| (df['Profit']>350)]
df22

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
56	475	40909	East	Small Market	Darjeeling	Leaves	Tea	Connecticut	Regular	50	80	60	130	50	821	73	14	47	123	26
57	475	40909	East	Small Market	Green Tea	Leaves	Tea	Connecticut	Regular	40	60	40	100	38	777	56	10	35	94	21
58	475	40909	East	Small Market	Decaf Espresso	Beans	Espresso	Connecticut	Decaf	50	70	40	120	55	312	75	18	30	130	45
59	860	40909	East	Small Market	Caffe Mocha	Beans	Espresso	Connecticut	Regular	70	100	20	170	75	522	105	68	7	180	98
60	475	40909	East	Small Market	Colombian	Beans	Coffee	Connecticut	Regular	110	180	130	290	124	961	186	40	115	310	71
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4243	206	41609	West	Small Market	Caffe Latte	Beans	Espresso	Washington	Regular	20	30	20	50	24	567	32	7	19	60	19
4244	509	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Washington	Regular	60	80	30	140	65	403	80	24	34	155	57
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45
4246	360	41609	West	Small Market	Colombian	Beans	Coffee	Washington	Regular	80	120	80	200	72	461	104	23	86	188	46
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

2908 rows × 20 columns

d23 = df22.groupby('Market Size')['Sales'].mean()
d23

Market Size
Major Market    271.202970
Small Market    149.397787
Name: Sales, dtype: float64

d24 = df22.groupby('Market Size')['COGS'].mean()
d24

Market Size
Major Market    120.265347
Small Market     66.000000
Name: COGS, dtype: float64

d23.plot(kind='area',color='maroon')
d24.plot(kind='area',color='yellow')
plt.title('area chart of budget and cogs mean based on market size')
plt.xlabel('market size')
plt.ylabel('budget and cogs mean')
plt.show()

plt.bar(d23.index,d23,color='maroon')
plt.bar(d24.index,d24,color='red')
plt.show()

d28 = df.groupby(['Market','Product Type'])['Sales'].mean()
d28

Market   Product Type
Central  Coffee          179.895833
         Espresso        207.295139
         Herbal Tea      202.047619
         Tea             203.500000
East     Coffee          337.148810
         Espresso        201.654167
         Herbal Tea      191.486111
         Tea             121.882576
South    Coffee          173.223958
         Espresso        156.215278
         Herbal Tea      133.734375
West     Coffee          185.410256
         Espresso        194.188889
         Herbal Tea      231.692308
         Tea             200.611111
Name: Sales, dtype: float64

d28.plot(kind='bar',color='orange')
plt.title('market and product type based sales mean')
plt.ylabel('sales mean')
plt.show()

dt = df.groupby('Market Size')['Sales'].mean()
dt

Market Size
Major Market    249.495892
Small Market    155.137579
Name: Sales, dtype: float64

dm = df.groupby('Product Type')['Sales'].mean()
dm

Product Type
Coffee        205.329545
Espresso      189.622449
Herbal Tea    196.225379
Tea           179.971875
Name: Sales, dtype: float64

plt.bar(dt.index,dt,color='yellow')
plt.bar(dm.index,dm,color='green')
plt.xlabel('Product Type and Market Size')
plt.ylabel('sales mean')
plt.xticks(rotation=60)
plt.show()

sot = df.groupby(['Product','Type'])['Budget Sales','Budget Profit'].sum()
sot

<ipython-input-71-52431f5cd96a>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  sot = df.groupby(['Product','Type'])['Budget Sales','Budget Profit'].sum()

		Budget Sales	Budget Profit
Product	Type
Amaretto	Regular	27200	7020
Caffe Latte	Regular	30540	10100
Caffe Mocha	Regular	84600	23280
Chamomile	Decaf	63840	24920
Colombian	Regular	134380	57800
Darjeeling	Regular	57360	22860
Decaf Espresso	Decaf	75720	29460
Decaf Irish Cream	Decaf	67040	19060
Earl Grey	Regular	50900	17600
Green Tea	Regular	25340	3800
Lemon	Decaf	78300	27020
Mint	Decaf	28320	6780
Regular Espresso	Regular	22620	9060

sot.plot(kind='line',color='green',marker='^')
plt.title('Product and Type wise budget sales and profit sum')
plt.xlabel('Product and Type')
plt.ylabel('Budget sales and Budget profit')
plt.xticks(rotation=90)
plt.show()

sit = df.groupby(['Product','Type'])['Budget COGS'].sum()
sit

Product            Type   
Amaretto           Regular    13420
Caffe Latte        Regular    12820
Caffe Mocha        Regular    36820
Chamomile          Decaf      25360
Colombian          Regular    51240
Darjeeling         Regular    22880
Decaf Espresso     Decaf      31660
Decaf Irish Cream  Decaf      32220
Earl Grey          Regular    21040
Green Tea          Regular    13220
Lemon              Decaf      32560
Mint               Decaf      15140
Regular Espresso   Regular     9500
Name: Budget COGS, dtype: int64

sit.plot(kind='line',color='green',marker='^')
plt.title('Product and Type wise budget COGS sum')
plt.xlabel('Product and Type')
plt.ylabel('Budget COGS sum')
plt.xticks(rotation=90)
plt.show()

####SEABORN

 Depict Product based on Market Size on a count plot<br>
2) Depict State based on Market on a count plot<br>
3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>
4) Depict Market vs Budget COGS on a boxplot<br>
5) Depict Product vs Margin on a boxplot based on Market Size<br>
6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling<br>
7) Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<br>
8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
9)  Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
10) sns.pairplot(d67)
plt.show()<br>
11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17)  Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>

d88 = df['Market Size'].value_counts()
d88

Small Market    2544
Major Market    1704
Name: Market Size, dtype: int64

d89 = df['Product'].value_counts()
d89

Lemon                480
Colombian            480
Caffe Mocha          480
Decaf Espresso       408
Chamomile            384
Darjeeling           384
Decaf Irish Cream    384
Earl Grey            288
Green Tea            288
Caffe Latte          216
Amaretto             192
Mint                 192
Regular Espresso      72
Name: Product, dtype: int64

#Q1
sns.countplot(df['Product'],hue=df['Market Size'])
plt.title('Product Count based on Market Size')
plt.xlabel('Product')
plt.ylabel('Product Count')
plt.xticks(rotation=90)
#plt.grid()
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

#Q2
sns.countplot(df['State'],hue=df['Market'])
plt.title('State Count based on Market')
plt.xlabel('State')
plt.ylabel('State Count')
plt.xticks(rotation=90)
#plt.grid()
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

#Q3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>

sns.violinplot(x='Product Type',y='Total Expenses',data=df,
               hue='Market')
plt.show()

#Q4) Depict Market vs Budget COGS on a boxplot<br>
sns.boxplot(df['Market'], y=df['Budget COGS'])
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

#Q5) Depict Product vs Margin on a boxplot based on Market Size<br>
sns.boxplot(df['Product'], y=df['Margin'],hue=df['Market Size'])
plt.xticks(rotation=90)
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

#Q6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling
d44 = df[(df['Product']=='Colombina') | (df['Product']=='Darjeeling')]
d44                                        

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
9	719	40909	Central	Major Market	Darjeeling	Leaves	Tea	Colorado	Regular	40	70	20	110	58	338	72	22	17	130	55
18	217	40909	Central	Major Market	Darjeeling	Leaves	Tea	Illinois	Regular	40	70	50	110	54	456	80	15	53	134	27
27	712	40909	Central	Small Market	Darjeeling	Leaves	Tea	Iowa	Regular	190	210	140	400	228	1459	228	63	141	456	87
36	314	40909	Central	Small Market	Darjeeling	Leaves	Tea	Missouri	Regular	30	40	10	70	35	196	47	11	9	82	38
46	567	40909	Central	Major Market	Darjeeling	Leaves	Tea	Ohio	Regular	70	110	60	180	84	651	126	27	68	210	58
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4192	435	41609	West	Small Market	Darjeeling	Leaves	Tea	Utah	Regular	30	50	0	80	49	335	69	44	-6	126	73
4204	323	41609	West	Major Market	Darjeeling	Leaves	Tea	California	Regular	90	130	110	220	123	959	179	34	199	322	45
4216	775	41609	West	Small Market	Darjeeling	Leaves	Tea	Nevada	Regular	180	240	150	420	247	1744	329	81	319	614	114
4227	541	41609	West	Small Market	Darjeeling	Leaves	Tea	Oregon	Regular	20	30	30	50	29	490	44	8	37	78	19
4238	253	41609	West	Small Market	Darjeeling	Leaves	Tea	Washington	Regular	30	40	20	70	41	320	66	12	45	114	36

384 rows × 20 columns

sns.violinplot(x='Market',y='Budget Sales',data=d44,
               hue='Product Type')
plt.show()

#Q7Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<

d80 = df[(df['State']=='California')|(df['State']=='Nevada')]
d80

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
133	661	40909	West	Major Market	Caffe Latte	Beans	Espresso	California	Regular	210	220	150	430	228	1459	228	63	140	456	88
134	818	40909	West	Major Market	Caffe Mocha	Beans	Espresso	California	Regular	100	140	20	240	105	725	145	95	17	250	128
135	213	40909	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	220	300	200	520	234	1310	312	77	203	546	109
136	510	40909	West	Major Market	Chamomile	Leaves	Herbal Tea	California	Decaf	80	120	100	200	95	821	139	26	102	234	37
137	310	40909	West	Major Market	Lemon	Leaves	Herbal Tea	California	Decaf	170	220	160	390	207	965	245	64	160	452	85
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4222	775	41609	West	Small Market	Caffe Latte	Beans	Espresso	Nevada	Regular	0	40	30	40	0	344	43	0	46	46	12
4223	775	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Nevada	Regular	10	20	10	30	16	851	25	4	15	44	15
4224	775	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Nevada	Decaf	20	30	20	50	23	807	35	6	25	62	18
4225	702	41609	West	Small Market	Colombian	Beans	Coffee	Nevada	Regular	40	60	20	100	39	250	49	14	1	94	48
4226	775	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Nevada	Decaf	30	50	30	80	31	1009	38	9	12	74	30

552 rows × 20 columns

sns.boxplot(d80['Product'], y=d80['Inventory'],hue=df['Product Type'])
plt.xticks(rotation=90)
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

#Q8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br

d67 = df[(df['Type']=='Regular')|(df['Product Type']=='Coffee')]
d67

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
0	719	40909	Central	Major Market	Amaretto	Beans	Coffee	Colorado	Regular	90	130	100	220	89	777	130	24	94	219	36
1	970	40909	Central	Major Market	Colombian	Beans	Coffee	Colorado	Regular	80	110	80	190	83	623	107	27	68	190	39
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
3	303	40909	Central	Major Market	Green Tea	Leaves	Tea	Colorado	Regular	30	50	30	80	44	623	56	14	30	100	26
4	303	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Colorado	Regular	60	90	70	150	54	456	80	15	54	134	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4240	509	41609	West	Small Market	Green Tea	Leaves	Tea	Washington	Regular	40	50	-10	90	55	627	76	49	-4	140	79
4243	206	41609	West	Small Market	Caffe Latte	Beans	Espresso	Washington	Regular	20	30	20	50	24	567	32	7	19	60	19
4244	509	41609	West	Small Market	Caffe Mocha	Beans	Espresso	Washington	Regular	60	80	30	140	65	403	80	24	34	155	57
4246	360	41609	West	Small Market	Colombian	Beans	Coffee	Washington	Regular	80	120	80	200	72	461	104	23	86	188	46
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

2784 rows × 20 columns

d67[['Budget Sales','Budget Profit', 'Budget COGS', 'Sales', 'Profit','COGS']]

	Budget Sales	Budget Profit	Budget COGS	Sales	Profit	COGS
0	220	100	90	219	94	89
1	190	80	80	190	68	83
2	240	110	100	234	101	95
3	80	30	30	100	30	44
4	150	70	60	134	54	54
...	...	...	...	...	...	...
4240	90	-10	40	140	-4	55
4243	50	20	20	60	19	24
4244	140	30	60	155	34	65
4246	200	80	80	188	86	72
4247	290	50	120	266	30	105

2784 rows × 6 columns

sns.pairplot(d67)
plt.show()

#Q9 Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>

d99 = df[(df['Profit']>250)]
d99

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
76	413	40909	East	Major Market	Colombian	Beans	Coffee	Massachusetts	Regular	60	400	360	460	72	558	422	23	367	494	55
90	716	40909	East	Major Market	Colombian	Beans	Coffee	New York	Regular	260	380	260	640	271	2101	407	94	262	678	145
143	559	40909	West	Major Market	Colombian	Beans	Coffee	California	Regular	340	500	370	840	271	2101	407	94	262	678	145
253	774	40940	East	Major Market	Colombian	Beans	Coffee	Massachusetts	Regular	70	370	330	440	75	235	395	24	340	470	55
265	585	40940	East	Major Market	Regular Espresso	Beans	Espresso	New York	Regular	210	350	280	560	228	1402	352	63	264	580	88
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4208	661	41609	West	Major Market	Lemon	Leaves	Herbal Tea	California	Decaf	220	260	170	480	241	1321	284	74	279	559	96
4212	209	41609	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	240	310	210	550	247	1744	329	81	321	614	113
4214	530	41609	West	Major Market	Colombian	Beans	Coffee	California	Regular	330	500	350	830	279	2642	420	97	402	745	149
4216	775	41609	West	Small Market	Darjeeling	Leaves	Tea	Nevada	Regular	180	240	150	420	247	1744	329	81	319	614	114
4220	702	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Nevada	Decaf	210	280	170	490	224	1191	310	73	288	569	116

181 rows × 20 columns

d99[['Budget Sales', 'Budget Profit', 'Budget COGS', 'Sales', 'Profit', 'COGS']]

	Budget Sales	Budget Profit	Budget COGS	Sales	Profit	COGS
76	460	360	60	494	367	72
90	640	260	260	678	262	271
143	840	370	340	678	262	271
253	440	330	70	470	340	75
265	560	280	210	580	264	228
...	...	...	...	...	...	...
4208	480	170	220	559	279	241
4212	550	210	240	614	321	247
4214	830	350	330	745	402	279
4216	420	150	180	614	319	247
4220	490	170	210	569	288	224

181 rows × 6 columns

#sns.pairplot(d99)
#plt.show()

#Q10Generate Pairplot for the Coffee dataset where Market is Western or Central based and Product Line is beans based on Type including fields- COGS, Sales, Proift and Total Expenses<br>

d100 = df[(df['Market']=='Western')|(df['Market']=='Central')&(df['Product Line']=='Beans')]
d100

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
0	719	40909	Central	Major Market	Amaretto	Beans	Coffee	Colorado	Regular	90	130	100	220	89	777	130	24	94	219	36
1	970	40909	Central	Major Market	Colombian	Beans	Coffee	Colorado	Regular	80	110	80	190	83	623	107	27	68	190	39
2	970	40909	Central	Major Market	Decaf Irish Cream	Beans	Coffee	Colorado	Decaf	100	140	110	240	95	821	139	26	101	234	38
4	303	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Colorado	Regular	60	90	70	150	54	456	80	15	54	134	26
5	720	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Colorado	Decaf	80	130	80	210	72	558	108	23	53	180	55
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4122	262	41609	Central	Small Market	Caffe Mocha	Beans	Espresso	Wisconsin	Regular	100	120	80	220	80	1079	96	24	74	188	46
4123	608	41609	Central	Small Market	Decaf Espresso	Beans	Espresso	Wisconsin	Decaf	30	40	30	70	24	567	32	7	19	60	19
4124	920	41609	Central	Small Market	Amaretto	Beans	Coffee	Wisconsin	Regular	60	80	30	140	65	403	80	24	33	155	58
4125	262	41609	Central	Small Market	Colombian	Beans	Coffee	Wisconsin	Regular	100	140	30	240	105	716	145	95	30	266	125
4126	414	41609	Central	Small Market	Decaf Irish Cream	Beans	Coffee	Wisconsin	Decaf	80	110	70	190	83	575	112	27	85	208	55

672 rows × 20 columns

b=d100[['COGS','Sales','Total Expenses','Profit']]
print(b)

      COGS  Sales  Total Expenses  Profit
0       89    219              36      94
1       83    190              39      68
2       95    234              38     101
4       54    134              26      54
5       72    180              55      53
...    ...    ...             ...     ...
4122    80    188              46      74
4123    24     60              19      19
4124    65    155              58      33
4125   105    266             125      30
4126    83    208              55      85

[672 rows x 4 columns]

sns.pairplot(b)
plt.show()

11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17)  Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>

#Q11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>

d94 = df[(df['Market']!='Central')&(df['Type']=='Decaf')]
d94

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
58	475	40909	East	Small Market	Decaf Espresso	Beans	Espresso	Connecticut	Decaf	50	70	40	120	55	312	75	18	30	130	45
61	860	40909	East	Small Market	Lemon	Leaves	Herbal Tea	Connecticut	Decaf	60	80	40	140	85	494	105	32	40	190	65
62	475	40909	East	Small Market	Mint	Leaves	Herbal Tea	Connecticut	Decaf	50	80	50	130	78	965	92	24	47	170	45
65	754	40909	East	Major Market	Decaf Espresso	Beans	Espresso	Florida	Decaf	70	100	60	170	77	432	103	25	50	180	53
67	754	40909	East	Major Market	Decaf Irish Cream	Beans	Coffee	Florida	Decaf	80	110	70	190	84	500	116	26	67	200	49
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4237	541	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Oregon	Decaf	40	80	50	120	41	435	60	13	37	108	35
4241	253	41609	West	Small Market	Chamomile	Leaves	Herbal Tea	Washington	Decaf	100	160	110	260	115	1166	174	37	156	308	69
4242	425	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Washington	Decaf	40	70	50	110	49	845	71	13	68	128	25
4245	360	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Washington	Decaf	70	100	60	170	80	1079	96	24	76	188	45
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

1224 rows × 20 columns

sns.stripplot(x="Product",
              y='Sales',data=d94,hue='Market')
plt.xticks(rotation=90)
plt.show()

#Q12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales>300

mini = df[(df['Market']!='Western')&(df['Sales']>300)]
mini

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
6	970	40909	Central	Major Market	Chamomile	Leaves	Herbal Tea	Colorado	Decaf	140	160	110	300	170	1091	171	47	99	341	72
11	217	40909	Central	Major Market	Colombian	Beans	Coffee	Illinois	Regular	150	210	130	360	144	862	201	47	111	345	90
13	309	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Illinois	Regular	270	370	260	640	234	1310	312	77	203	546	109
14	630	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Illinois	Decaf	260	270	180	530	228	1459	228	63	140	456	88
25	641	40909	Central	Small Market	Chamomile	Leaves	Herbal Tea	Iowa	Decaf	200	280	200	480	234	1310	312	77	202	546	110
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4220	702	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Nevada	Decaf	210	280	170	490	224	1191	310	73	288	569	116
4221	775	41609	West	Small Market	Mint	Leaves	Herbal Tea	Nevada	Decaf	120	170	120	290	127	830	185	40	181	332	63
4229	503	41609	West	Small Market	Green Tea	Leaves	Tea	Oregon	Regular	90	140	100	230	134	690	186	41	180	341	65
4234	971	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Oregon	Decaf	150	140	80	290	153	1319	153	42	129	326	66
4241	253	41609	West	Small Market	Chamomile	Leaves	Herbal Tea	Washington	Decaf	100	160	110	260	115	1166	174	37	156	308	69

674 rows × 20 columns

sns.stripplot(x="State",
              y='Budget Profit',data=d94,hue='Product Type')
plt.xticks(rotation=90)
plt.show()

#Q13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small

ddd = df[(df['Market Size']=='Small Market') & (df['Sales']>200)]
ddd

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
25	641	40909	Central	Small Market	Chamomile	Leaves	Herbal Tea	Iowa	Decaf	200	280	200	480	234	1310	312	77	202	546	110
26	563	40909	Central	Small Market	Lemon	Leaves	Herbal Tea	Iowa	Decaf	80	120	90	200	95	608	139	30	86	234	53
27	712	40909	Central	Small Market	Darjeeling	Leaves	Tea	Iowa	Regular	190	210	140	400	228	1459	228	63	141	456	87
28	319	40909	Central	Small Market	Earl Grey	Leaves	Tea	Iowa	Regular	210	270	160	480	245	1419	301	93	175	546	126
60	475	40909	East	Small Market	Colombian	Beans	Coffee	Connecticut	Regular	110	180	130	290	124	961	186	40	115	310	71
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4228	971	41609	West	Small Market	Earl Grey	Leaves	Tea	Oregon	Regular	60	100	60	160	88	817	133	29	108	236	60
4229	503	41609	West	Small Market	Green Tea	Leaves	Tea	Oregon	Regular	90	140	100	230	134	690	186	41	180	341	65
4234	971	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Oregon	Decaf	150	140	80	290	153	1319	153	42	129	326	66
4241	253	41609	West	Small Market	Chamomile	Leaves	Herbal Tea	Washington	Decaf	100	160	110	260	115	1166	174	37	156	308	69
4247	206	41609	West	Small Market	Decaf Irish Cream	Beans	Coffee	Washington	Decaf	120	170	50	290	105	716	145	95	30	266	125

506 rows × 20 columns

sns.swarmplot(x="Market",
              y='Total Expenses',data=ddd,hue='Type')
plt.xticks(rotation=90)
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 19.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

#Q15) Depict Budget Sales vs Sales on a Joint Plot

sns.jointplot(x=df['Budget Sales'],y=df['Sales'],kind='reg',color='yellow')
plt.show()

#Q16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<

dss = df[(df['Sales']>300) | (df['Profit']>300)]
dss

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
6	970	40909	Central	Major Market	Chamomile	Leaves	Herbal Tea	Colorado	Decaf	140	160	110	300	170	1091	171	47	99	341	72
11	217	40909	Central	Major Market	Colombian	Beans	Coffee	Illinois	Regular	150	210	130	360	144	862	201	47	111	345	90
13	309	40909	Central	Major Market	Caffe Mocha	Beans	Espresso	Illinois	Regular	270	370	260	640	234	1310	312	77	203	546	109
14	630	40909	Central	Major Market	Decaf Espresso	Beans	Espresso	Illinois	Decaf	260	270	180	530	228	1459	228	63	140	456	88
25	641	40909	Central	Small Market	Chamomile	Leaves	Herbal Tea	Iowa	Decaf	200	280	200	480	234	1310	312	77	202	546	110
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4220	702	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Nevada	Decaf	210	280	170	490	224	1191	310	73	288	569	116
4221	775	41609	West	Small Market	Mint	Leaves	Herbal Tea	Nevada	Decaf	120	170	120	290	127	830	185	40	181	332	63
4229	503	41609	West	Small Market	Green Tea	Leaves	Tea	Oregon	Regular	90	140	100	230	134	690	186	41	180	341	65
4234	971	41609	West	Small Market	Decaf Espresso	Beans	Espresso	Oregon	Decaf	150	140	80	290	153	1319	153	42	129	326	66
4241	253	41609	West	Small Market	Chamomile	Leaves	Herbal Tea	Washington	Decaf	100	160	110	260	115	1166	174	37	156	308	69

674 rows × 20 columns

sns.jointplot(x=dss['Total Expenses'],y=dss['Sales'],color='green',kind='scatter')
plt.show()

#Q17)  Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex

diy = df[(df['Market']=='East')&(df['Type']=='Regular')]
diy

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
56	475	40909	East	Small Market	Darjeeling	Leaves	Tea	Connecticut	Regular	50	80	60	130	50	821	73	14	47	123	26
57	475	40909	East	Small Market	Green Tea	Leaves	Tea	Connecticut	Regular	40	60	40	100	38	777	56	10	35	94	21
59	860	40909	East	Small Market	Caffe Mocha	Beans	Espresso	Connecticut	Regular	70	100	20	170	75	522	105	68	7	180	98
60	475	40909	East	Small Market	Colombian	Beans	Coffee	Connecticut	Regular	110	180	130	290	124	961	186	40	115	310	71
63	954	40909	East	Major Market	Darjeeling	Leaves	Tea	Florida	Regular	30	50	40	80	32	821	48	8	28	80	20
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4159	518	41609	East	Major Market	Earl Grey	Leaves	Tea	New York	Regular	120	190	150	310	123	959	179	34	197	322	46
4160	585	41609	East	Major Market	Green Tea	Leaves	Tea	New York	Regular	50	80	60	130	54	601	79	15	77	142	27
4161	845	41609	East	Major Market	Caffe Mocha	Beans	Espresso	New York	Regular	130	-70	-210	60	135	3641	-69	122	-332	70	155
4162	518	41609	East	Major Market	Regular Espresso	Beans	Espresso	New York	Regular	240	400	320	640	250	723	407	70	463	700	95
4163	315	41609	East	Major Market	Colombian	Beans	Coffee	New York	Regular	250	370	250	620	279	2642	420	97	402	745	149

600 rows × 20 columns

#sns.jointplot(x=diy['Type'],y=diy['Budget COGS'],kind='hex',color='red')
#plt.show()

#Q18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<

dw = df[(df['Market']!='Central') & (df['Sales']>500)]
dw

	Area Code	Date	Market	Market Size	Product	Product Line	Product Type	State	Type	Budget COGS	Budget Margin	Budget Profit	Budget Sales	COGS	Inventory	Margin	Marketing	Profit	Sales	Total Expenses
90	716	40909	East	Major Market	Colombian	Beans	Coffee	New York	Regular	260	380	260	640	271	2101	407	94	262	678	145
135	213	40909	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	220	300	200	520	234	1310	312	77	203	546	109
143	559	40909	West	Major Market	Colombian	Beans	Coffee	California	Regular	340	500	370	840	271	2101	407	94	262	678	145
152	775	40909	West	Small Market	Darjeeling	Leaves	Tea	Nevada	Regular	150	220	150	370	234	1310	312	77	203	546	109
265	585	40940	East	Major Market	Regular Espresso	Beans	Espresso	New York	Regular	210	350	280	560	228	1402	352	63	264	580	88
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4212	209	41609	West	Major Market	Decaf Espresso	Beans	Espresso	California	Decaf	240	310	210	550	247	1744	329	81	321	614	113
4214	530	41609	West	Major Market	Colombian	Beans	Coffee	California	Regular	330	500	350	830	279	2642	420	97	402	745	149
4216	775	41609	West	Small Market	Darjeeling	Leaves	Tea	Nevada	Regular	180	240	150	420	247	1744	329	81	319	614	114
4217	775	41609	West	Small Market	Earl Grey	Leaves	Tea	Nevada	Regular	180	180	100	360	250	1820	251	70	233	534	94
4220	702	41609	West	Small Market	Lemon	Leaves	Herbal Tea	Nevada	Decaf	210	280	170	490	224	1191	310	73	288	569	116

225 rows × 20 columns

sns.jointplot(x=dw['Sales'],y=dw['Marketing'],kind='kde',color='brown')
plt.show()